Click here for interactive plots:

https://kforthman.shinyapps.io/COVID19_Interactive_Plots/

Click here to see how cities and counties overlap:

https://kforthman.shinyapps.io/500citiescounties

Compare COVID stats to Neighborhood Factors

data.cor <- cor(county.Demo_and_Covid.allcounties[,-1], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

Compare COVID stats to 500 cities data and Neighborhood Factors

data.cor2 <- cor(county.Demo_and_Covid.500counties[,-c(1:2)], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor2, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

corrplot.mixed(data.cor2[7:13,c(1:5, 14:42,6)], upper = 'ellipse', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

—-Linear Mixed Effects Model —-

this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)", data = county.Demo_and_Covid.500counties)
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -1263.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2444 -0.3497 -0.0832  0.1955  6.3116 
## 
## Random effects:
##  Groups   Name        Variance    Std.Dev.
##  stateID  (Intercept) 0.000002079 0.001442
##  Residual             0.000013235 0.003638
## Number of obs: 186, groups:  stateID, 34
## 
## Fixed effects:
##                                  Estimate     Std. Error             df
## (Intercept)                 -0.0137678378   0.0097218158  81.7517433858
## Affluence                    0.0043808501   0.0010921351 117.7613495334
## Singletons.in.Tract          0.0005614038   0.0009014385 157.0024855703
## Seniors.in.Tract             0.0002196420   0.0011761408 163.9898953488
## African.Americans.in.Tract   0.0011297070   0.0010046179 162.5732458815
## Noncitizens.in.Tract         0.0010587247   0.0007511198 127.7482847045
## High.BP                      0.0001366756   0.0001873187 125.7707667950
## Binge.Drinking               0.0002332299   0.0001630168  52.8113561971
## Cancer                      -0.0012106131   0.0011052476 122.5239395969
## Asthma                       0.0008843084   0.0005708531  55.8297901669
## Heart.Disease                0.0019095163   0.0013399841  89.8216617288
## COPD                        -0.0004553023   0.0011073913  87.9195289066
## Smoking                     -0.0000452274   0.0002305638  94.4650899643
## Diabetes                    -0.0006012543   0.0005426570  91.0538300290
## No.Physical.Activity        -0.0000152144   0.0002094629 105.4440670776
## Obesity                      0.0002643811   0.0001764230 126.3845809812
## Poor.Sleeping.Habits        -0.0000435769   0.0001630557 139.4188310145
## Poor.Mental.Health          -0.0000885639   0.0004429728  37.6633161202
## Testing_Rate                 0.0000006661   0.0000002627  45.9929173709
## Hospitalization_Rate        -0.0000824114   0.0000952874  31.1767480819
##                            t value Pr(>|t|)    
## (Intercept)                 -1.416 0.160522    
## Affluence                    4.011 0.000106 ***
## Singletons.in.Tract          0.623 0.534328    
## Seniors.in.Tract             0.187 0.852089    
## African.Americans.in.Tract   1.125 0.262453    
## Noncitizens.in.Tract         1.410 0.161109    
## High.BP                      0.730 0.466966    
## Binge.Drinking               1.431 0.158404    
## Cancer                      -1.095 0.275520    
## Asthma                       1.549 0.127008    
## Heart.Disease                1.425 0.157614    
## COPD                        -0.411 0.681964    
## Smoking                     -0.196 0.844906    
## Diabetes                    -1.108 0.270788    
## No.Physical.Activity        -0.073 0.942234    
## Obesity                      1.499 0.136480    
## Poor.Sleeping.Habits        -0.267 0.789670    
## Poor.Mental.Health          -0.200 0.842610    
## Testing_Rate                 2.535 0.014699 *  
## Hospitalization_Rate        -0.865 0.393715    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence    0.078                                                        
## Sngltns.n.T  0.008  0.042                                                 
## Snrs.n.Trct  0.528  0.363  0.172                                          
## Afrcn.Am..T  0.155  0.158 -0.406  0.151                                   
## Nnctzns.n.T  0.033  0.098  0.069  0.089 -0.107                            
## High.BP     -0.053  0.213  0.048  0.073 -0.098  0.389                     
## Bing.Drnkng -0.309 -0.164 -0.283 -0.156  0.065  0.001  0.129              
## Cancer      -0.574 -0.161  0.196 -0.296 -0.075 -0.150 -0.345 -0.100       
## Asthma      -0.391 -0.188 -0.237 -0.202  0.076  0.053  0.170 -0.010  0.054
## Heart.Dises -0.151  0.094 -0.297 -0.146  0.254 -0.119  0.005  0.058 -0.485
## COPD         0.573 -0.005  0.150  0.262 -0.028  0.285  0.135  0.085 -0.266
## Smoking     -0.138  0.162 -0.184 -0.098 -0.036  0.038 -0.058 -0.292  0.076
## Diabetes     0.108 -0.333 -0.100 -0.207 -0.299 -0.288 -0.530  0.053  0.235
## N.Physcl.Ac -0.216 -0.008  0.090 -0.022 -0.035 -0.225 -0.067  0.111  0.484
## Obesity      0.016  0.408  0.437  0.304  0.130  0.174 -0.101 -0.232  0.093
## Pr.Slpng.Hb -0.432 -0.401  0.148 -0.336 -0.334 -0.020 -0.173  0.113  0.121
## Pr.Mntl.Hlt -0.352  0.277 -0.068 -0.044  0.091 -0.190 -0.050  0.082  0.325
## Testing_Rat  0.241 -0.077  0.005  0.033  0.033 -0.024 -0.033 -0.023 -0.213
## Hsptlztn_Rt -0.140 -0.190 -0.068 -0.223 -0.073 -0.097 -0.062 -0.135  0.016
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H Pr.M.H
## Affluence                                                                 
## Sngltns.n.T                                                               
## Snrs.n.Trct                                                               
## Afrcn.Am..T                                                               
## Nnctzns.n.T                                                               
## High.BP                                                                   
## Bing.Drnkng                                                               
## Cancer                                                                    
## Asthma                                                                    
## Heart.Dises  0.282                                                        
## COPD        -0.391 -0.564                                                 
## Smoking      0.086  0.201 -0.495                                          
## Diabetes    -0.128 -0.315 -0.061  0.223                                   
## N.Physcl.Ac  0.040 -0.376 -0.016 -0.331 -0.106                            
## Obesity     -0.275 -0.084  0.156 -0.194 -0.372 -0.048                     
## Pr.Slpng.Hb  0.086  0.239 -0.168 -0.062 -0.030 -0.112 -0.168              
## Pr.Mntl.Hlt -0.240  0.094 -0.468  0.081  0.018  0.057  0.059 -0.164       
## Testing_Rat -0.348 -0.040  0.226  0.140  0.120 -0.322  0.138 -0.149 -0.149
## Hsptlztn_Rt  0.096  0.104 -0.107  0.073  0.009 -0.004 -0.013 -0.003 -0.091
##             Tstn_R
## Affluence         
## Sngltns.n.T       
## Snrs.n.Trct       
## Afrcn.Am..T       
## Nnctzns.n.T       
## High.BP           
## Bing.Drnkng       
## Cancer            
## Asthma            
## Heart.Dises       
## COPD              
## Smoking           
## Diabetes          
## N.Physcl.Ac       
## Obesity           
## Pr.Slpng.Hb       
## Pr.Mntl.Hlt       
## Testing_Rat       
## Hsptlztn_Rt  0.106
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + (1 | stateID)", data = county.Demo_and_Covid.500counties)

print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -2448.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7251 -0.3792 -0.0778  0.2647  6.7047 
## 
## Random effects:
##  Groups   Name        Variance    Std.Dev.
##  stateID  (Intercept) 0.000007639 0.002764
##  Residual             0.000012331 0.003512
## Number of obs: 326, groups:  stateID, 51
## 
## Fixed effects:
##                                Estimate   Std. Error           df t value
## (Intercept)                 -0.02272680   0.00792033 194.66846846  -2.869
## Affluence                    0.00290302   0.00071753 302.85783904   4.046
## Singletons.in.Tract          0.00081203   0.00066931 300.68924552   1.213
## Seniors.in.Tract             0.00041518   0.00084551 304.35453945   0.491
## African.Americans.in.Tract   0.00175146   0.00081741 306.66138653   2.143
## Noncitizens.in.Tract         0.00179018   0.00066040 273.33539751   2.711
## High.BP                     -0.00001546   0.00014808 299.60754399  -0.104
## Binge.Drinking               0.00039750   0.00015603 161.67302985   2.548
## Cancer                      -0.00034360   0.00086920 268.11913096  -0.395
## Asthma                       0.00072204   0.00051738 143.52223176   1.396
## Heart.Disease                0.00308413   0.00111621 214.10545211   2.763
## COPD                        -0.00126301   0.00084507 208.34195421  -1.495
## Smoking                     -0.00020991   0.00019519 253.74331189  -1.075
## Diabetes                    -0.00114613   0.00041818 270.94653985  -2.741
## No.Physical.Activity         0.00031281   0.00016806 240.19605631   1.861
## Obesity                      0.00023618   0.00013584 307.91957919   1.739
## Poor.Sleeping.Habits         0.00025424   0.00013088 297.87722422   1.942
## Poor.Mental.Health          -0.00015554   0.00043944 104.89407784  -0.354
##                             Pr(>|t|)    
## (Intercept)                  0.00457 ** 
## Affluence                  0.0000662 ***
## Singletons.in.Tract          0.22599    
## Seniors.in.Tract             0.62375    
## African.Americans.in.Tract   0.03292 *  
## Noncitizens.in.Tract         0.00714 ** 
## High.BP                      0.91689    
## Binge.Drinking               0.01178 *  
## Cancer                       0.69293    
## Asthma                       0.16500    
## Heart.Disease                0.00623 ** 
## COPD                         0.13654    
## Smoking                      0.28323    
## Diabetes                     0.00654 ** 
## No.Physical.Activity         0.06393 .  
## Obesity                      0.08309 .  
## Poor.Sleeping.Habits         0.05302 .  
## Poor.Mental.Health           0.72408    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence   -0.053                                                        
## Sngltns.n.T -0.054  0.042                                                 
## Snrs.n.Trct  0.392  0.293  0.073                                          
## Afrcn.Am..T  0.241  0.076 -0.404  0.202                                   
## Nnctzns.n.T -0.072  0.153  0.125  0.058 -0.191                            
## High.BP     -0.094  0.158  0.098  0.008 -0.232  0.325                     
## Bing.Drnkng -0.490 -0.038 -0.205 -0.067  0.041 -0.076  0.148              
## Cancer      -0.494 -0.095  0.231 -0.171 -0.074 -0.065 -0.330 -0.018       
## Asthma      -0.270 -0.095 -0.262 -0.122 -0.015  0.212  0.050  0.010 -0.157
## Heart.Dises -0.059  0.079 -0.302 -0.132  0.213 -0.055  0.001  0.034 -0.603
## COPD         0.479  0.008  0.130  0.171 -0.007  0.156  0.057  0.058 -0.211
## Smoking     -0.042  0.105 -0.119 -0.138 -0.104  0.159 -0.082 -0.327  0.156
## Diabetes     0.036 -0.302 -0.078 -0.132 -0.230 -0.251 -0.447  0.074  0.369
## N.Physcl.Ac -0.116  0.035  0.102  0.079  0.059 -0.275  0.004  0.128  0.335
## Obesity     -0.066  0.382  0.398  0.201  0.133  0.193 -0.103 -0.146  0.118
## Pr.Slpng.Hb -0.384 -0.349  0.162 -0.325 -0.321 -0.046 -0.156  0.087  0.028
## Pr.Mntl.Hlt -0.353  0.184 -0.008  0.024  0.052 -0.164  0.029  0.130  0.416
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H
## Affluence                                                          
## Sngltns.n.T                                                        
## Snrs.n.Trct                                                        
## Afrcn.Am..T                                                        
## Nnctzns.n.T                                                        
## High.BP                                                            
## Bing.Drnkng                                                        
## Cancer                                                             
## Asthma                                                             
## Heart.Dises  0.335                                                 
## COPD        -0.321 -0.492                                          
## Smoking      0.144  0.084 -0.475                                   
## Diabetes    -0.106 -0.434 -0.006  0.277                            
## N.Physcl.Ac -0.021 -0.359  0.088 -0.274 -0.168                     
## Obesity     -0.124 -0.020  0.091 -0.220 -0.375 -0.044              
## Pr.Slpng.Hb  0.000  0.239 -0.092 -0.169 -0.061 -0.153 -0.115       
## Pr.Mntl.Hlt -0.438 -0.065 -0.390 -0.029  0.071 -0.088  0.024 -0.080

Testing Rate

testing.data.state <- compiled.stats[[length(daily_filenames)]][, c("Province_State", "Testing_Rate")]
testing.data.state <- testing.data.state[!is.na(testing.data.state$Testing_Rate),]
testing.data.state <- testing.data.state[order(testing.data.state$Testing_Rate),]

col.state <- rep("pink", nrow(testing.data.state))

avg.test.rate <- mean(testing.data.state$Testing_Rate, na.rm = T)

col.state[testing.data.state$Testing_Rate < avg.test.rate] <- "grey"
col.state[testing.data.state$Province_State == "Oklahoma"] <- "lightblue"

par(mar = c(5,6,4,2))
barplot(testing.data.state$Testing_Rate, names.arg = testing.data.state$Province_State, horiz = T, main = "Testing Rate by State", las = 2, cex.axis = 1, cex.names = 0.5, col = col.state, border = F, xlab = "Total number of people tested per 100,000 persons.")
abline(v = avg.test.rate, col = "red")
text(x = avg.test.rate + 10, y = 1, labels = "Average Testing Rate", adj = c(0, 0.5), col = "red")

Pink highlights the last 14 days.

day.first.case <- min(which(US.total$cases.total > 100))
n.days <- nrow(US.total)

twoweek.col <- c(rep("grey", n.days-day.first.case-13), rep("pink", 14))

par(mar = c(5,5,4,2))
barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$rise.cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Cases of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$rise.deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Deaths of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)